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Learning P2P Lending Credit Evaluation Bayesian Network from Missing Data

Volume 15, Number 6, June 2019, pp. 1591-1599
DOI: 10.23940/ijpe.19.06.p10.15911599

Yali Lva,b, Jianai Wua, Junzhong Miaoa, Weixin Hua, and Tong Jinga

aSchool of Information Management, Shanxi University of Finance and Economics, Taiyuan, 030006, China
bKey Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, 030006, China


(Submitted on March 20, 2019; Revised on April 3, 2019; Accepted on June 7, 2019)


Credit evaluation is an important issue for investors in the financial field. However, there is a large amount of missing data in the P2P lending platform. To evaluate borrowers' credit from missing data, a credit evaluation Bayesian network model learning algorithm is proposed based on domain knowledge. Specifically, we first give a credit evaluation Bayesian network (CEBN) model to represent the borrowers' attributions and the relationships between attributions, and then we design the CEBN learning algorithm based on domain knowledge. Furthermore, we analyze and discuss the time complexity of the algorithm. Finally, the experimental results demonstrate that the CEBN model has good interpretability, learning performance, and evaluation performance by comparing it with other methods.


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